Automated Application of the &amp;#8220;7-point checklist&amp;#8221; Diagnosis Method for Skin Lesions: Estimation of Chromatic and Shape Parameters.

Machine Learning in Medical Imaging

Atkins

2011

Abstract. This paper presents a novel approach to analysis and classification of skin lesions based on their growth pattern. Our method constructs a tree structure for every lesion by repeatedly subdividing the image into sub-images using color based clustering. In this method, segmentation which is a challenging task is not required. The obtained multi-scale tree structure provides a framework that allows us to extract a variety of features, based on the appearance of the tree structure or sub-images corresponding to nodes of the tree. Preliminary features (the number of nodes, leaves, and depth of the tree, and 9 compactness indices of the dark spots represented by the sub-images associated with each node of the tree) are used to train a supervised learning algorithm. Results show the strength of the method in classifying lesions into malignant and benign classes. We achieved Precision of 0.855, Recall of 0.849, and F-measure of 0.834 using 3-layer perceptron and Precision of 0.829, Recall of 0.832, and F-measure of 0.817 using AdaBoost on a dataset containing 112 malignant and 298 benign lesion dermoscopic images.

Section: Resultsmentioning

confidence: 99%

Tree Structured Model of Skin Lesion Growth Pattern via Color Based Cluster Analysis

KhakAbi

Machine Learning in Medical Imaging

Atkins

2011

“…However, they did not publish the details of their algorithm. Betta et al [30] developed a method in which streaks were detected by simultaneously looking for occurrence of nger-like tracks along the contour of a lesion, and brown pigmentation for the corresponding region. Dividing an image into 16 sub-images, they computed, in each sub-image, the irregularity of the lesion border and also the hue component of the original color image in the HSV color space [31].…”

Section: Previous Workmentioning

confidence: 99%

Detection and Analysis of Irregular Streaks in Dermoscopic Images of Skin Lesions

Sadeghi

IEEE Trans. Med. Imaging

McLean

et al. 2013

113

Abstract-Irregular streaks are important clues for Melanoma (a potentially fatal form of skin cancer) diagnosis using dermoscopy images. This paper extends our previous algorithm to identify the absence or presence of streaks in a skin lesions, by further analyzing the appearance of detected streak lines, and performing a three-way classi cation for streaks, Absent, Regular, and Irregular, in a pigmented skin lesion. In addition, the directional pattern of detected lines is analyzed to extract their orientation features in order to detect the underlying pattern. The method uses a graphical representation to model the geometric pattern of valid streaks and the distribution and coverage of the structure. Using these proposed features of the valid streaks along with the color and texture features of the entire lesion, an accuracy of 76.1% and weighted average area under ROC curve (AUC) of 85% is achieved for classifying dermoscopy images into streaks Absent, Regular, or Irregular on 945 images compiled from atlases and the internet without any exclusion criteria. This challenging dataset is the largest validation dataset for streaks detection and classi cation published to date. The data set has also been applied to the two-class sub-problems of Absent/Present classi cation (accuracy of 78.3% with AUC of 83.2%) and to Regular/Irregular classi cation (accuracy 83.6% with AUC of 88.9%). When the method was tested on a cleaned subset of 300 images randomly selected from the 945 images, the AUC increased to 91.8%, 93.2% and 90.9% for the Absent/Regular/Irregular, Absent/Present, and Regular/Irregular problems, respectively.

“…However, the automatic detection of streaks has only recently been investigated [5,6]. Streaks on dermoscopy images usually are difficult to detect since they are not perfect linear structures, but often fuzzy and low-contrast oriented intensities.…”

Section: Mathematical Definitionmentioning

confidence: 99%

Oriented Pattern Analysis for Streak Detection in Dermoscopy Images

Sadeghi

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

McLean

et al. 2012

Abstract. There is an increasing demand for automated detection and analysis of dermoscopy structures and malignancy clues such as streaks in dermoscopy images, for computer-aided early diagnosis of deadly melanoma. This paper presents a novel approach for streak detection and visualization on dermoscopic images. We tackle the detection of streaks by means of ridge and valley estimation. Orientation estimation and correction is applied to detect low contrast and fuzzy streaks lines, and candidate streaks are used to classify dermoscopy images into streaks Absent or Present with the AUC of 90.5% on 300 dermoscopy images. Our approach can also detect starburst pattern of regular streaks using detected linear structures with accuracy of 81.5% and AUC of 87.7%.